master-data-management

Master data management resolves inconsistent records across CRM, ERP, and HRIS systems by applying deterministic and probabilistic matching rules to create a trusted golden record. It defines survivorship strategies and governance workflows to maintain data consistency across multiple source systems.

The best way to choose an MDM architecture is to evaluate registry, consolidation, coexistence, and centralized patterns based on your integration needs. This Skill helps justify the selection by specifying matching rules, stewardship responsibilities, and hub-spoke integration patterns for your master data.

Survivorship rules resolve conflicting attribute values from duplicate records by determining which source system data survives into the golden record. You specify these strategies alongside deterministic and probabilistic matching rules to maintain trusted master data across your organization.

You need master data governance workflows for entity resolution when managing multiple source systems like CRM, ERP, and procurement that require consistent customer, product, and location data. These workflows define stewardship responsibilities to maintain data quality and audit trails for the golden record.

You define matching rules for a golden record by specifying deterministic and probabilistic criteria to identify and link duplicate entities across platforms. This process includes mapping integration patterns like hub-spoke or coexistence to consistently consolidate core data from source systems.